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No Parkland Meshblock Summary

  df_mesh_lockdown_summary %>%
    filter(as.numeric(area)==0) %>%
  { . } -> df_none

df_none %>%
  count( MB_CATEGORY_NAME_2016, sort=TRUE) %>%
  gt::gt() %>%
  gt::tab_header(
           title = "Meshblocks with no parks within 5km",
           subtitle = "Number by category"
)
Meshblocks with no parks within 5km
Number by category
MB_CATEGORY_NAME_2016 n
Residential 29 
df_none %>%
  select(-starts_with('mc'), -State) %>%
  summarise( across( where(is.numeric),
                    .fns=list(mean=mean, max=max, min=min, sd=sd, total = sum))) %>%
  gt::gt() %>%
gt::tab_header(
               title = "Meshblocks with no parks within 5km",
               subtitle = "Summary statistics"
)
Meshblocks with no parks within 5km
Summary statistics
area_mean area_max area_min area_sd area_total AREA_ALBERS_SQKM_mean AREA_ALBERS_SQKM_max AREA_ALBERS_SQKM_min AREA_ALBERS_SQKM_sd AREA_ALBERS_SQKM_total Dwelling_mean Dwelling_max Dwelling_min Dwelling_sd Dwelling_total Person_mean Person_max Person_min Person_sd Person_total
0 [m^2] 0 [m^2] 0 [m^2] 0 0 [m^2] 0.3056414 3.4271 0.0293 0.6711457 8.8636 41.41379 60 9 12.84389 1201 106.9655 157 14 34.05928 3102

LGA level summary

df_mesh_lockdown_summary  %>%
  group_by(lga_name) %>%
  select(-starts_with('mc'), -State) %>%
  mutate( area = units::set_units( area, 'km^2') ) %>%
  summarise(across(.cols=where(is_numeric),
                   .fns=list(mean=mean, max=max, min=min, sd=sd, total = sum),
                   .names = "{col}.{fn}"), number_mesh_blocks = n(),
            .groups='drop') %>%
  { . } -> df_lga_summary
Warning: Deprecated

Warning: Deprecated

Warning: Deprecated

Warning: Deprecated

Warning: Deprecated

Warning: Deprecated

Warning: Deprecated

Warning: Deprecated
df_lga_summary %>%
  gt::gt() %>%
gt::tab_header(
               title = "Lga Summary"
)
Lga Summary
lga_name area.mean area.max area.min area.sd area.total AREA_ALBERS_SQKM.mean AREA_ALBERS_SQKM.max AREA_ALBERS_SQKM.min AREA_ALBERS_SQKM.sd AREA_ALBERS_SQKM.total Dwelling.mean Dwelling.max Dwelling.min Dwelling.sd Dwelling.total Person.mean Person.max Person.min Person.sd Person.total number_mesh_blocks
Banyule 15.185289 [km^2] 17.79422 [km^2] 7.87307193 [km^2] 1.5369448 19270.132 [km^2] 0.036987943 1.3652 0.0037 0.050459776 46.9377 38.83924 137 0 11.95744 49287 94.24586 250 0 32.30976 119598 1269
Bayside 6.219211 [km^2] 10.68724 [km^2] 3.43332336 [km^2] 1.6493926 6181.896 [km^2] 0.027557847 0.1105 0.0037 0.010533698 27.3925 40.28672 370 0 18.28782 40045 95.59960 564 3 36.75206 95026 994
Boroondara 11.191267 [km^2] 16.42813 [km^2] 6.84276928 [km^2] 2.8202078 19562.336 [km^2] 0.026556236 0.1809 0.0015 0.013855586 46.4203 38.61213 312 0 18.74855 67494 92.83810 530 0 37.44842 162281 1748
Brimbank 10.736161 [km^2] 14.63235 [km^2] 4.39925419 [km^2] 2.3418011 18101.168 [km^2] 0.033929893 0.2297 0.0013 0.016238864 57.2058 40.34816 213 0 13.49730 68027 112.89146 377 0 37.48626 190335 1686
Cardinia 3.021159 [km^2] 18.43710 [km^2] 0.00000000 [km^2] 3.2156669 2311.187 [km^2] 0.120625229 10.2854 0.0088 0.429598685 92.2783 38.21830 331 0 20.46497 29237 102.82484 520 0 51.14711 78661 765
Casey 9.052639 [km^2] 28.16408 [km^2] 0.10346466 [km^2] 5.0145369 22332.861 [km^2] 0.064600851 4.3807 0.0065 0.172223090 159.3703 40.25415 584 0 19.44779 99307 118.19497 1563 0 52.74135 291587 2467
Darebin 11.138544 [km^2] 16.27913 [km^2] 8.39442754 [km^2] 2.0232598 17075.388 [km^2] 0.023339400 0.1478 0.0011 0.011839653 35.7793 40.13633 214 0 15.11722 61529 92.23875 479 0 35.09408 141402 1533
Frankston 11.831855 [km^2] 16.12092 [km^2] 3.71528637 [km^2] 2.6173162 17191.685 [km^2] 0.045083551 1.0460 0.0042 0.079356067 65.5064 38.40468 431 0 17.10738 55802 90.47970 693 0 38.25040 131467 1453
Glen Eira 7.832578 [km^2] 11.77597 [km^2] 5.12486060 [km^2] 1.5133682 11874.188 [km^2] 0.021501781 0.0739 0.0023 0.009833396 32.5967 38.71768 303 0 14.34187 58696 90.85488 295 0 32.29669 137736 1516
Greater Dandenong 14.013030 [km^2] 24.86233 [km^2] 2.99986017 [km^2] 3.9685152 19534.164 [km^2] 0.027157461 0.2899 0.0019 0.016519147 37.8575 38.26686 377 0 17.49722 53344 106.07676 524 0 40.70106 147871 1394
Hobsons Bay 10.920534 [km^2] 19.48648 [km^2] 4.46273352 [km^2] 4.5572304 9697.434 [km^2] 0.027201239 0.1503 0.0026 0.010766162 24.1547 41.18131 163 0 13.90562 36569 98.25450 391 0 33.88325 87250 888
Hume 9.426658 [km^2] 20.41986 [km^2] 0.00000000 [km^2] 5.3743911 14884.693 [km^2] 0.055784547 2.7442 0.0034 0.148271405 88.0838 40.67891 265 0 14.43367 64232 119.25902 783 0 45.75360 188310 1579
Kingston 10.591121 [km^2] 15.24164 [km^2] 7.11436661 [km^2] 1.8220434 17380.030 [km^2] 0.025631140 0.3020 0.0029 0.013375572 42.0607 37.54966 222 0 15.12078 61619 89.49482 369 8 34.17293 146861 1641
Knox 16.248975 [km^2] 29.50606 [km^2] 7.81893385 [km^2] 5.7690297 22634.822 [km^2] 0.043232161 0.5604 0.0031 0.029689784 60.2224 41.66475 216 0 16.86122 58039 108.34314 418 0 38.80162 150922 1393
Manningham 13.022915 [km^2] 18.87721 [km^2] 5.32729042 [km^2] 4.0142667 14442.413 [km^2] 0.052050135 0.4655 0.0024 0.052534972 57.7236 39.30929 333 0 16.69148 43594 100.19748 442 0 33.69702 111119 1109
Maribyrnong 9.216707 [km^2] 11.67654 [km^2] 7.64732478 [km^2] 0.7592285 7751.251 [km^2] 0.018892033 0.1250 0.0018 0.010196186 15.8882 40.98811 372 0 22.41438 34471 94.85375 625 0 43.66590 79772 841
Maroondah 6.784671 [km^2] 17.95823 [km^2] 4.76147480 [km^2] 1.8258573 7700.602 [km^2] 0.040433040 0.7314 0.0054 0.031928654 45.8915 38.78502 359 8 15.12772 44021 96.07577 428 18 36.11853 109046 1135
Melbourne 13.917569 [km^2] 17.83142 [km^2] 7.19365050 [km^2] 2.3036747 9157.760 [km^2] 0.010052888 0.3884 0.0005 0.016267494 6.6148 81.43313 820 0 104.64713 53583 143.93161 1420 0 171.52913 94707 658
Melton 5.845543 [km^2] 13.39463 [km^2] 0.02024515 [km^2] 2.2614614 6710.683 [km^2] 0.063486760 4.6791 0.0044 0.231159116 72.8828 39.23258 265 0 16.03247 45039 113.58624 1145 0 53.47677 130397 1148
Monash 10.658986 [km^2] 24.90446 [km^2] 5.44214681 [km^2] 3.6826422 18685.202 [km^2] 0.031278779 0.1692 0.0024 0.012689424 54.8317 39.50314 314 0 16.44179 69249 100.89218 554 0 35.75911 176864 1753
Moonee Valley 10.867900 [km^2] 12.87149 [km^2] 8.41506036 [km^2] 1.0230332 12671.972 [km^2] 0.025014151 0.1251 0.0013 0.012249724 29.1665 41.37564 789 0 28.02625 48244 96.42882 1490 0 57.08910 112436 1166
Moreland 10.982315 [km^2] 14.61052 [km^2] 8.08687463 [km^2] 1.2519347 17890.191 [km^2] 0.022397790 0.1207 0.0015 0.011369870 36.4860 42.03131 443 0 23.08106 68469 96.70043 678 0 42.10126 157525 1629
Mornington Peninsula 5.251301 [km^2] 15.17988 [km^2] 0.44753341 [km^2] 2.6990688 11726.155 [km^2] 0.068692611 3.6804 0.0047 0.126870661 153.3906 37.65607 552 0 18.34183 84086 64.69727 763 0 45.99725 144469 2233
Nillumbik 9.363364 [km^2] 17.03398 [km^2] 0.00000000 [km^2] 4.3935067 4419.508 [km^2] 0.093530720 1.1567 0.0099 0.126969096 44.1465 38.16102 71 3 11.84666 18012 107.47034 232 10 37.63969 50726 472
Port Phillip 9.471658 [km^2] 12.89079 [km^2] 5.54027647 [km^2] 1.7034196 11119.726 [km^2] 0.009634923 0.0488 0.0010 0.005850779 11.3114 44.46934 364 0 32.21854 52207 78.28705 565 0 50.96849 91909 1174
Stonnington 10.377284 [km^2] 14.77288 [km^2] 6.45121772 [km^2] 2.5691104 12463.118 [km^2] 0.016405995 0.1043 0.0009 0.012034248 19.7036 40.01166 443 0 26.55481 48054 78.45545 709 0 45.18010 94225 1201
Whitehorse 8.645928 [km^2] 14.20004 [km^2] 6.38896615 [km^2] 1.3822301 13798.901 [km^2] 0.030590038 0.2579 0.0010 0.014826582 48.8217 40.58459 216 0 16.41082 64773 99.53195 488 0 36.70984 158853 1596
Whittlesea 9.383794 [km^2] 21.11696 [km^2] 1.19949349 [km^2] 3.6457455 16477.943 [km^2] 0.046959738 6.4906 0.0058 0.173330437 82.4613 38.80011 424 0 18.67791 68133 108.57289 783 0 45.04374 190654 1756
Wyndham 8.283654 [km^2] 29.92811 [km^2] 0.12219149 [km^2] 5.5221152 15581.553 [km^2] 0.055817863 7.8791 0.0073 0.299822653 104.9934 39.24349 279 0 15.62235 73817 113.46146 1070 0 48.84813 213421 1881
Yarra 16.158732 [km^2] 17.84010 [km^2] 13.27009926 [km^2] 1.0648759 13880.351 [km^2] 0.011739814 0.1022 0.0013 0.007821915 10.0845 47.14785 576 0 48.07148 40500 92.83818 841 0 84.53188 79748 859
Yarra Ranges 13.876811 [km^2] 59.04844 [km^2] 0.00000000 [km^2] 10.0935565 17498.658 [km^2] 0.107572958 1.8395 0.0056 0.139485038 135.6495 40.09040 279 3 14.56882 50554 101.58921 448 7 35.98934 128104 1261 
df_mesh_lockdown_summary  %>%
  write_csv('output/lockdown_greenspace_mesh_detail.csv')

df_lga_summary %>%
  write_csv('output/lockdown_greenspace_lga_summary.csv')

SA2 level summary

df_mesh_lockdown_summary  %>%
  group_by(SA2_MAIN16) %>%
  select(-starts_with('mc'), -State) %>%
  mutate( area = units::set_units( area, 'km^2') ) %>%
  summarise(across(.cols=where(is_numeric),
                   .fns=list(mean=mean, max=max, min=min, sd=sd, total = sum),
                   .names = "{col}.{fn}"), number_mesh_blocks = n(),
            .groups='drop') %>%
  { . } -> df_sa2_summary
Warning: Deprecated

Warning: Deprecated

Warning: Deprecated

Warning: Deprecated

Warning: Deprecated

Warning: Deprecated

Warning: Deprecated

Warning: Deprecated
df_sa2_summary %>%
  head(10) %>%
  gt::gt() %>%
gt::tab_header(
               title = "SA2 Summary",
               subtitle = "First 10 rows"
)
SA2 Summary
First 10 rows
SA2_MAIN16 area.mean area.max area.min area.sd area.total AREA_ALBERS_SQKM.mean AREA_ALBERS_SQKM.max AREA_ALBERS_SQKM.min AREA_ALBERS_SQKM.sd AREA_ALBERS_SQKM.total Dwelling.mean Dwelling.max Dwelling.min Dwelling.sd Dwelling.total Person.mean Person.max Person.min Person.sd Person.total number_mesh_blocks
204011061 42.13817 [km^2] 59.04844 [km^2] 11.999212 [km^2] 26.1659374 126.4145 [km^2] 0.23680000 0.4042 0.0977 0.155197390 0.7104 17.00000 23 10 6.557439 51 28.00000 37 16 10.81665 84 3
206011105 11.22203 [km^2] 13.82834 [km^2] 10.181837 [km^2] 0.8463643 2928.9494 [km^2] 0.01345326 0.0351 0.0015 0.006225212 3.5113 42.68199 177 5 22.246746 11140 88.89655 345 7 40.75226 23202 261
206011106 12.72169 [km^2] 14.34703 [km^2] 10.897082 [km^2] 0.9692451 1475.7163 [km^2] 0.01409828 0.0370 0.0017 0.006733059 1.6354 48.14655 443 0 56.793639 5585 93.51724 678 4 81.06857 10848 116
206011107 11.56507 [km^2] 12.14974 [km^2] 10.788757 [km^2] 0.3132748 1873.5407 [km^2] 0.01666852 0.0531 0.0015 0.008884808 2.7003 40.65432 217 10 19.390182 6586 82.62346 385 15 38.58459 13385 162
206011108 10.43298 [km^2] 11.68780 [km^2] 8.086875 [km^2] 0.6856222 2639.5436 [km^2] 0.02256957 0.0946 0.0027 0.009758878 5.7101 42.08696 241 0 19.585596 10648 101.04743 394 0 38.73718 25565 253
206011109 10.75059 [km^2] 11.88591 [km^2] 9.542034 [km^2] 0.6560649 935.3017 [km^2] 0.02919425 0.0508 0.0081 0.008556411 2.5399 42.85057 72 17 11.069053 3728 111.44828 217 24 33.06363 9696 87
206021110 13.80918 [km^2] 15.52856 [km^2] 11.967993 [km^2] 1.0753853 1394.7270 [km^2] 0.02071881 0.0452 0.0057 0.008669299 2.0926 38.26733 75 13 11.290608 3865 87.00000 193 32 28.69286 8787 101
206021111 14.03233 [km^2] 16.39193 [km^2] 12.154639 [km^2] 0.8716260 3830.8253 [km^2] 0.01700879 0.0604 0.0017 0.007729779 4.6434 38.27106 134 3 15.577362 10448 86.81319 248 7 36.36293 23700 273
206021112 12.03072 [km^2] 14.96160 [km^2] 10.346722 [km^2] 0.9405670 2610.6660 [km^2] 0.01750968 0.0444 0.0026 0.007826259 3.7996 38.51613 98 11 11.424915 8358 82.70046 257 19 31.50966 17946 217
206031113 11.03825 [km^2] 11.66311 [km^2] 10.025369 [km^2] 0.3607965 1600.5467 [km^2] 0.01984138 0.0606 0.0025 0.009725815 2.8770 43.61379 210 12 23.884614 6324 96.86207 451 36 47.87623 14045 145 
df_sa2_summary %>%
  write_csv('output/lockdown_greenspace_sa2_summary.csv')

SA1 level summary

df_mesh_lockdown_summary  %>%
  group_by(SA1_MAIN16) %>%
  select(-starts_with('mc'), -State) %>%
  mutate( area = units::set_units( area, 'km^2') ) %>%
  summarise(across(.cols=where(is_numeric),
                   .fns=list(mean=mean, max=max, min=min, sd=sd, total = sum),
                   .names = "{col}.{fn}"), number_mesh_blocks = n(),
            .groups='drop') %>%
  { . } -> df_sa1_summary
Warning: Deprecated

Warning: Deprecated

Warning: Deprecated

Warning: Deprecated

Warning: Deprecated

Warning: Deprecated

Warning: Deprecated

Warning: Deprecated
df_sa1_summary %>%
  head(10) %>%
  gt::gt() %>%
  gt::tab_header(
                 title = "SA1 Summary",
                 subtitle = "First 10 rows"
  )
SA1 Summary
First 10 rows
SA1_MAIN16 area.mean area.max area.min area.sd area.total AREA_ALBERS_SQKM.mean AREA_ALBERS_SQKM.max AREA_ALBERS_SQKM.min AREA_ALBERS_SQKM.sd AREA_ALBERS_SQKM.total Dwelling.mean Dwelling.max Dwelling.min Dwelling.sd Dwelling.total Person.mean Person.max Person.min Person.sd Person.total number_mesh_blocks
8.265415 [km^2] 8.265415 [km^2] 8.265415 [km^2] NA 8.265415 [km^2] 0.0266000 0.0266 0.0266 NA 0.0266 48.00 48 48 NA 48 83.0000 83 83 NA 83 1
20401106101 42.138174 [km^2] 59.048443 [km^2] 11.999212 [km^2] 26.16593741 126.414522 [km^2] 0.2368000 0.4042 0.0977 0.155197390 0.7104 17.00 23 10 6.557439 51 28.0000 37 16 10.816654 84 3
20601110501 10.761375 [km^2] 10.796213 [km^2] 10.726537 [km^2] 0.04926838 21.522749 [km^2] 0.0205000 0.0229 0.0181 0.003394113 0.0410 54.50 59 50 6.363961 109 109.5000 115 104 7.778175 219 2
20601110502 10.406138 [km^2] 10.504986 [km^2] 10.327990 [km^2] 0.05991859 83.249102 [km^2] 0.0129125 0.0214 0.0050 0.005480729 0.1033 30.75 40 15 7.535630 246 72.7500 106 44 23.407874 582 8
20601110503 10.643974 [km^2] 10.732194 [km^2] 10.564890 [km^2] 0.06359407 53.219871 [km^2] 0.0124400 0.0174 0.0096 0.002926260 0.0622 38.00 53 27 9.643651 190 89.6000 154 69 36.239481 448 5
20601110504 11.122997 [km^2] 11.245029 [km^2] 11.037630 [km^2] 0.10845205 33.368992 [km^2] 0.0168000 0.0226 0.0094 0.006743886 0.0504 45.00 52 38 7.000000 135 100.3333 138 60 39.068316 301 3
20601110505 10.736944 [km^2] 10.902372 [km^2] 10.589545 [km^2] 0.12858726 42.947776 [km^2] 0.0171250 0.0218 0.0121 0.004618351 0.0685 40.50 50 30 9.146948 162 89.0000 113 60 21.924112 356 4
20601110506 10.401720 [km^2] 10.540552 [km^2] 10.293592 [km^2] 0.09496873 52.008602 [km^2] 0.0139600 0.0188 0.0073 0.004723135 0.0698 34.20 51 25 10.034939 171 78.4000 92 55 14.135063 392 5
20601110507 10.751806 [km^2] 10.966954 [km^2] 10.582393 [km^2] 0.16113143 53.759029 [km^2] 0.0165800 0.0236 0.0089 0.006171467 0.0829 46.20 59 34 9.418068 231 100.0000 130 69 26.804850 500 5
20601110508 11.406475 [km^2] 11.671566 [km^2] 11.145402 [km^2] 0.21835673 57.032376 [km^2] 0.0125000 0.0165 0.0086 0.003248846 0.0625 39.80 53 23 10.825895 199 75.8000 86 54 13.084342 379 5 
df_sa1_summary %>%
  write_csv('output/lockdown_greenspace_sa1_summary.csv')

Map

map_sa1 %>%
  inner_join(df_sa1_summary, by = "SA1_MAIN16" ) %>%
  mutate( area.mean = area.mean %>% as.numeric() ) %>%
  ggplot() +
  geom_sf( aes( fill = area.mean ),lwd=0 ) +
  scale_fill_viridis_c(option = "plasma") +
  ggtitle('Km^2 greenspace access within 5km of meshblock average for SA1')

Version Author Date
e1388a3 Dennis Wollersheim 2020-08-06
a71385f Dennis Wollersheim 2020-08-05
b83cc3d Dennis Wollersheim 2020-08-05
1b1df89 Dennis Wollersheim 2020-08-05 
map_sa2 %>%
  inner_join(df_sa2_summary, by = "SA2_MAIN16" ) %>%
  mutate( area.mean = area.mean %>% as.numeric() ) %>%
  ggplot() +
  geom_sf( aes( fill = area.mean ),lwd=0 ) +
  scale_fill_viridis_c(option = "plasma") +
  ggtitle('Km^2 greenspace access within 5km of meshblock average for SA2')

Version Author Date
e1388a3 Dennis Wollersheim 2020-08-06

Green Map

map_mesh %>%
  inner_join(df_mesh_lockdown_summary_parks, by = "MB_CODE16" ) %>%
  { . } -> df_parks

map_sa1 %>%
  inner_join(df_sa1_summary, by = "SA1_MAIN16" ) %>%
  mutate( area.mean = area.mean %>% as.numeric() ) %>%
  ggplot() +
  geom_sf( aes( fill = area.mean ),lwd=0 ) +
  geom_sf(data=df_parks, fill=RColorBrewer::brewer.pal(3,'Greens')[2]) +
  scale_fill_viridis_c(option = "plasma") +
  ggtitle('Km^2 greenspace access within 5km of meshblock. Green is parks')

Version Author Date
e1388a3 Dennis Wollersheim 2020-08-06
a71385f Dennis Wollersheim 2020-08-05
b83cc3d Dennis Wollersheim 2020-08-05
a69a370 Dennis Wollersheim 2020-08-05
1b1df89 Dennis Wollersheim 2020-08-05 
map_sa2 %>%
  inner_join(df_sa2_summary, by = "SA2_MAIN16" ) %>%
  mutate( area.mean = area.mean %>% as.numeric() ) %>%
  ggplot() +
  geom_sf( aes( fill = area.mean ),lwd=0 ) +
  geom_sf(data=df_parks, fill=RColorBrewer::brewer.pal(3,'Greens')[2]) +
  scale_fill_viridis_c(option = "plasma") +
  ggtitle('Km^2 greenspace access within 5km of meshblock. Green is parks')

Version Author Date
e1388a3 Dennis Wollersheim 2020-08-06

Interactive Map SA2 level

map_levels = c('0-5km^2', '5-10km^2', '10-15km^2','15-20km^2','>20km^2')
map_colors = RColorBrewer::brewer.pal(6,'YlOrRd')[2:6]
map_palette = colorFactor(map_colors, levels=map_levels)

map_sa2 %>%
  inner_join(df_sa2_summary, by = "SA2_MAIN16" ) %>%
  mutate( area.mean = area.mean %>% as.numeric() ) %>%
  mutate( area_factor = cut( area.mean, breaks= c(0, 5,10,15,20,99),
                            labels= map_levels )) %>%
  { . } -> df_leaflet


df_leaflet %>%
  leaflet() %>%
  addPolygons(stroke = FALSE, smoothFactor = 0.2, fillOpacity = 1,
              color = ~map_palette(area_factor)) %>%
  addLegend("bottomright", pal = map_palette, values = ~area_factor,
            title = "Mean park area within 5km circle of mesh blocks within SA2",
            opacity = 1
            ) %>%
  addPolygons( weight = .5, smoothFactor = 0.5,
              opacity = .5, fillOpacity = 0.0,
              data = map_lga) %>%
  addPolygons( weight = 1, smoothFactor = 0.5,
              opacity = 1, fillOpacity = 1,
              color = RColorBrewer::brewer.pal(3,'Greens')[2],
              data = df_parks)
Warning: sf layer has inconsistent datum (+proj=longlat +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +no_defs ).
Need '+proj=longlat +datum=WGS84'

Warning: sf layer has inconsistent datum (+proj=longlat +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +no_defs ).
Need '+proj=longlat +datum=WGS84'

Warning: sf layer has inconsistent datum (+proj=longlat +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +no_defs ).
Need '+proj=longlat +datum=WGS84'

Interactive Map SA1 level

map_levels = c('0-5km^2', '5-10km^2', '10-15km^2','15-20km^2','>20km^2')
map_colors = RColorBrewer::brewer.pal(6,'YlOrRd')[2:6]
map_palette = colorFactor(map_colors, levels=map_levels)

map_sa1 %>%
  inner_join(df_sa1_summary, by = "SA1_MAIN16" ) %>%
  mutate( area.mean = area.mean %>% as.numeric() ) %>%
  mutate( area_factor = cut( area.mean, breaks= c(0, 5,10,15,20,99),
                            labels= map_levels )) %>%
  { . } -> df_leaflet


df_leaflet %>%
  leaflet() %>%
  addPolygons(stroke = FALSE, smoothFactor = 0.2, fillOpacity = 1,
              color = ~map_palette(area_factor)) %>%
  addLegend("bottomright", pal = map_palette, values = ~area_factor,
            title = "Mean park area within 5km circle of mesh blocks within SA2",
            opacity = 1
  ) %>%
  addPolygons( weight = .5, smoothFactor = 0.5,
              opacity = .5, fillOpacity = 0.0,
              data = map_lga) %>%
  addPolygons( weight = 1, smoothFactor = 0.5,
              opacity = 1, fillOpacity = 1,
              color = RColorBrewer::brewer.pal(3,'Greens')[2],
              data = df_parks)
Warning: sf layer has inconsistent datum (+proj=longlat +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +no_defs ).
Need '+proj=longlat +datum=WGS84'

Warning: sf layer has inconsistent datum (+proj=longlat +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +no_defs ).
Need '+proj=longlat +datum=WGS84'

Warning: sf layer has inconsistent datum (+proj=longlat +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +no_defs ).
Need '+proj=longlat +datum=WGS84'

sessionInfo()
R version 4.0.2 (2020-06-22)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.4 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/openblas/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/libopenblasp-r0.2.20.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] rvest_0.3.5        xml2_1.3.2         leaflet_2.0.3.9000 workflowr_1.6.2   
 [5] cleangeo_0.2-3     maptools_1.0-1     rgeos_0.5-3        sp_1.4-2          
 [9] geohash_0.4.0      osrm_3.3.3         sf_0.9-3           janitor_2.0.1     
[13] rmarkdown_2.1      lubridate_1.7.8    geosphere_1.5-10   forcats_0.5.0     
[17] stringr_1.4.0      dplyr_1.0.0        purrr_0.3.4        readr_1.3.1       
[21] tidyr_1.1.0        tibble_3.0.3       ggplot2_3.3.2      tidyverse_1.3.0   
[25] drake_7.12.4       dotenv_1.0.2       conflicted_1.0.4   nvimcom_0.9-99    

loaded via a namespace (and not attached):
 [1] nlme_3.1-147       fs_1.4.1           RColorBrewer_1.1-2 filelock_1.0.2    
 [5] progress_1.2.2     httr_1.4.1         rprojroot_1.3-2    tools_4.0.2       
 [9] backports_1.1.8    R6_2.4.1           KernSmooth_2.23-17 DBI_1.1.0         
[13] colorspace_1.4-1   withr_2.2.0        tidyselect_1.1.0   prettyunits_1.1.1 
[17] compiler_4.0.2     git2r_0.27.1       Xmisc_0.2.1        cli_2.0.2         
[21] gt_0.2.1           labeling_0.3       sass_0.2.0         checkmate_2.0.0   
[25] scales_1.1.1       classInt_0.4-3     digest_0.6.25      foreign_0.8-79    
[29] txtq_0.2.3         pkgconfig_2.0.3    htmltools_0.5.0    dbplyr_1.4.3      
[33] htmlwidgets_1.5.1  rlang_0.4.7        readxl_1.3.1       rstudioapi_0.11   
[37] farver_2.0.3       generics_0.0.2     jsonlite_1.7.0     crosstalk_1.1.0.1 
[41] magrittr_1.5       Rcpp_1.0.5         munsell_0.5.0      fansi_0.4.1       
[45] lifecycle_0.2.0    stringi_1.4.6      whisker_0.4        yaml_2.2.1        
[49] snakecase_0.11.0   storr_1.2.1        grid_4.0.2         parallel_4.0.2    
[53] promises_1.1.1     crayon_1.3.4       lattice_0.20-41    haven_2.2.0       
[57] hms_0.5.3          knitr_1.28         pillar_1.4.6       igraph_1.2.5      
[61] base64url_1.4      reprex_0.3.0       glue_1.4.1         evaluate_0.14     
[65] modelr_0.1.7       vctrs_0.3.2        httpuv_1.5.4       cellranger_1.1.0  
[69] gtable_0.3.0       assertthat_0.2.1   xfun_0.16          broom_0.5.6       
[73] e1071_1.7-3        later_1.1.0.1      viridisLite_0.3.0  class_7.3-17      
[77] memoise_1.1.0      units_0.6-6        ellipsis_0.3.1